This invention relates to mobile wireless communication systems.
In a typical wireless communication system, each device, e.g., a user equipment (UE), a base station (BS), etc., has a local oscillator (LO) that defines a local time reference. It is crucial that the LOs of devices communicating with each other are aligned as precisely as possible. If two devices' LOs are not aligned, their time references drift in relation to each other, which can result in the devices' being no longer capable of receiving information properly from each other and in loss of the connection.
LO alignment is particularly important to wireless communication systems like wideband code division multiple access (WCDMA) mobile telephone systems and other digital mobile telephone systems. In such systems, the UE uses automatic frequency control (AFC) to adjust its LO so that it aligns well with the LOs of the BS(s) to which the UE is connected.
A WCDMA communication system uses direct-sequence spread-spectrum techniques. Pseudo-noise scrambling codes and orthogonal channelization codes separate BSs and physical communication channels, respectively, in the downlink (BS-to-UE) and uplink (UE-to-BS) directions. Scrambling and channelization codes are well known in the art, and WCDMA and other third-generation (3G) and future communication systems operate in accordance with standards promulgated by the Third Generation Partnership Project (3GPP).
The operation of a typical AFC involves studying communication-channel parameters over time and attempting to adjust the LO based on at least such parameters. For example, complex-valued estimates of the impulse response of a communication channel might be studied over time, and the adjustment of the LO may be done such that no rotation of the channel estimates is seen in the complex plane. That kind of adjustment is based on the fact that phase-plane rotation corresponds to relative frequency drift, which in turn corresponds to relative time reference drift.
FIG. 1 is a block diagram of an apparatus 100, which may be a portion of a typical UE, and depicts the operation of a typical AFC. A voltage-controlled crystal oscillator (VCXO) 102 generates the LO signal used by a receiver front end (RX Fe) 104 and a transmitter front end (TX Fe) (not shown). The frequency of the LO signal produced by the VCXO 102 is responsive to a control signal ferr generated by an AFC 106, and as illustrated, the control signal produced by the AFC 106 may be converted to an analog control voltage by a digital-to-analog converter (DAC) 108. The AFC 106 generates the control signal ferr based on channel estimates ĥf produced by a channel estimator 110, which typically provides such estimates to other devices in the UE. The channel estimator 110 generates the channel estimates from correlated signals provided by the “fingers” (correlators) of a RAKE processor 112, which generates the correlated signals from signals from the RX Fe 104 that have been digitized by an analog-to-digital converter (ADC), filtered, and otherwise processed in conditioning block 114.
Channel estimation and RAKE processing are well known in the art. Channel estimation generally involves multiplying the correlated signals with the complex conjugates of known signals (or pilots), and possibly averaging the results over time. U.S. Patent Application Publication No. 2005/0105647 by Wilhelmsson et al. for “Channel Estimation by Adaptive Interpolation” describes channel estimation in a communication system using orthogonal frequency division multiplex. RAKE processing, which typically includes correlating and combining, is described in, for example, John G. Proakis, “Digital Communications”, 3rd ed., pp. 797-806, McGraw-Hill; and U.S. Pat. No. 6,922,434 by Wang et al. for “Apparatus and Methods for Finger Delay Selection in Rake Receivers”. Signals from the channel estimator 110 and RAKE processor 112 are typically used in further processing operations carried out by the UE.
Some UEs have AFCs that can be selectively operated in either a high or a low speed mode. As depicted in FIG. 1, the speed mode in such UEs can be selected based on a control signal from a Doppler-spread estimator 116 that applies an argument- (or zero-) crossing algorithm or a level-crossing algorithm, for example, to signals received by the UE. Other UEs have only one speed mode, and thus do not need the Doppler estimator 116 for speed-mode adjustment, although such UEs may use Doppler estimates for other purposes. It will be understood that one or two speed modes are described here solely as examples; more than two speed modes can be used without departing from the scope of this invention.
The AFC 106 typically generates the control signal ferr in the following way, which is just one of many possible examples. In low-speed mode, one channel estimate ĥf is collected for each RAKE finger f during a given time period, e.g., during each of the successive time slots into which the received signal is organized, and in high-speed mode, five channel estimates per finger are collected in each slot. The AFC 106 uses the current channel estimate ĥf and a channel estimate previously collected ĥfprevious to determine a value y given by:
  y  =            ∑      f        ⁢                                        h            ^                    f                ⁡                  (                                    h              ^                        f            previous                    )                    *      where the asterisk denotes complex conjugation. The value y may be filtered, for example according to:yfilt=λ(y−yfiltprevious)+yfiltprevious where yfilt is the current filtered value, λ is a filter parameter, and yfiltprevious is the filtered value for the previous slot. In high-speed mode, yfiltprevious is the filtered value for the previous channel estimate. It will be understood that the filter state is appropriately initialized or reset whenever a UE frequency reference update (ΔfUE) or a speed-mode change occurs. The reported frequency error ferr is then given by:ferr=φ/2πΔt where the phase angle φ=arg(yfilt) and Δt is the time interval between two consecutive updates of the AFC (e.g., two consecutive collected channel estimates). For just two of many possible examples, Δt may be 1/1500 in low-speed mode (i.e., one slot is 1/1500 second) and 1/7500 in high-speed mode. These computations are conveniently called the “typical AFC computations” in this application.
In an AFC such as that described above, there is a risk of AFC wrap-around, which occurs when |Δf|>½Δt, where Δf is the actual frequency error and Δt is the time interval between two consecutive updates of the AFC. This corresponds to situations where the channel estimates rotate more than ±π between successive AFC updates, which result in the frequency error ferr reported by the AFC being in error by a multiple of 1/Δt Hz. For example, if the AFC updates once per slot, which is typical of low-speed or single-speed mode, then ½Δt=750 Hz. In the typical high-speed mode, the AFC is updated five times per slot, and then ½Δt=3750 Hz. It will be noted that the UE usually goes out-of-sync and loses the connection if the correct frequency reference is not restored within approximately 50-150 slots.
AFC wrap-around is a phenomenon that arises from the discrete sampling of the frequency error by the AFC and is particularly likely in situations where the relative speed of a transmitter and receiver is high and rapidly changing and the AFC is operating in low-speed mode. In the context of a UE in a 3G mobile telephone system, those situations include passing close by a BS (e.g., at a distance less than 10 meters (m) or so). In those situations, the actual frequency error Δf becomes Δf=2fD after passing the base station (assuming no frequency error before passing), where fD is the Doppler frequency shift given by fD=vfc/c, in which v is the relative velocity of the transmitter and receiver (e.g., a BS and a UE), fc is the carrier signal's frequency (about 2 GHz in some 3G mobile telephone systems), and c is the speed of light. Hence, an AFC wrap-around event can occur for velocities around and above 185 kilometers per hour (km/h) in the close-passing situation with Δt= 1/1500.
Results of simulations of the close-passing situation are depicted in FIGS. 2-4 for a one-tap, line-of-sight (LOS) (no fading) channel without interference. The UE was assumed to pass a BS at a distance of 2 m, and channel estimation was almost perfect, as the simulated signal-to-noise ratio (SNR) of the channel estimates was 17 dB. For the simulations, the AFC filter parameter was set to λ=0.02. The same settings apply to all simulation results shown in this application except as otherwise noted.
In FIGS. 2A, 3A, 4A, the dot-dash line shows the apparatus frequency reference (without the carrier frequency component), and the solid line shows the actual Doppler frequency shift. In FIGS. 2B, 3B, 4B, the dot-dash line shows the AFC reported frequency error ferr, and the solid line shows the actual frequency error. Frequency shift or error is shown on the vertical axes and time in slots is shown on the horizontal axes in the figures.
From FIGS. 2 and 3, it can be seen that the AFC managed to follow the frequency change at a relative velocity of 150 km/h (FIG. 2), but not at 350 km/h (FIG. 3), when the AFC was in low-speed mode. FIG. 4 shows high-speed mode at 350 km/h, where the correct frequency reference was successfully tracked.
As noted above, if the AFC is in high-speed mode, wrap-around occurs when Δf>3750 Hz, which corresponds to around and above 935 km/h in the close-passing situation. Thus, comparing FIGS. 3 and 4, if the AFC could be in or be put in high-speed mode at the right time, the AFC wrap-around problem would be solved for all currently realistic land-based velocities. Running the AFC in high-speed modes at all times is, however, not always preferable because then the AFC becomes more sensitive to noise and there may be unnecessary toggling in UE frequency compensations in high-speed mode.
Thus, some devices include Doppler estimators (see block 116 in FIG. 1) to determine the speed-mode for AFC operation. As described above, two different Doppler estimation algorithms, the level-crossing algorithm and the argument-crossing (or zero-crossing) algorithm, may be used. It will be understood, however, that in general any Doppler estimation algorithm known in the art may be used. All of these algorithms, however, have problems with detecting high-speed situations under LOS conditions.
The level-crossing algorithm counts the number of times the absolute value of the complex channel estimate crosses a given level. It is assumed that the higher the velocity is, the faster the fading is, and hence that the number of level crossings per unit time corresponds to the velocity. This is an accurate method as long as all of the paths (fingers) used by the AFC are Rayleigh-distributed. In LOS conditions, however, the strongest path is typically dominant and has a Ricean distribution, which is to say that the strongest path may be fading very weakly or hardly at all. In such conditions, a Doppler estimator that uses a level-crossing algorithm cannot detect high-velocity situations, and thus the AFC would remain in low-speed mode.
A variant of the argument-crossing algorithm counts the number of times that the complex channel estimate crosses either of the imaginary and real axes. It is assumed that the phase variations become faster with higher velocity, and hence the number of crossings per unit time is assumed to correspond to the velocity. This is also an accurate method as long as all of the paths involved are Rayleigh-fading. In LOS conditions, however, the phase shift due to Doppler shift (i.e., rotation in the complex plane) typically dominates the random phase variations that are due to Doppler spread. In such conditions, a Doppler estimator that uses an argument-crossing algorithm registers phase rotations that are mainly due to changes in Doppler shift, and thus is highly likely to underestimate the velocity, and the AFC would again remain in low-speed mode.
The risk of AFC wrap-around is high for devices that implement AFC either with only a low-speed mode of operation or with Doppler estimators that use the usual algorithms, which do not reliably detect high-speed situations in LOS environments. Therefore, there is a need for methods and apparatus that solve the AFC wrap-around problem.